「cause」の検索結果

Write blameless incident postmortems with timeline reconstruction, root cause analysis, action items, and preventive measures

日本語の概要は準備中です。原文の説明を表示しています。

Systematically trace bugs backward through call stack to find original trigger. Use when errors occur deep in execution and you need to trace back to find the original trigger.

日本語の概要は準備中です。原文の説明を表示しています。

Four-phase debugging framework that ensures root cause investigation before attempting fixes. Never jump to solutions. Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

日本語の概要は準備中です。原文の説明を表示しています。

Validate at every layer data passes through to make bugs impossible. Use when invalid data causes failures deep in execution, requiring validation at multiple system layers.

日本語の概要は準備中です。原文の説明を表示しています。

Делает ревью продуктовых метрик — тренды, аномалии, root causes и рекомендации к действиям. Включает декомпозицию North Star (L1/L2), диагностику retention-кривых, анализ воронки, разбор A/B-экспериментов, проверку соответствия OKR и фреймворк атрибуции аномалий. User-invoked only — do NOT auto-trigger. Triggers on /pm-metrics, "обзор метрик", "разбор воронки", "анализ удержания", "ретеншн", "A/B результаты", "review metrics", "DAU analysis", "retention analysis", "funnel analysis", "metric anomaly".

日本語の概要は準備中です。原文の説明を表示しています。

Triage a GitHub issue using diagnostics archives and logs. Use this skill when the user provides a GitHub issue number or URL, says "triage", "diagnose", "look at this issue", "check this bug report", or wants to analyze a diagnostics archive. Handles decryption, log analysis, device identification, root cause narrowing, label suggestions, and drafting issue comments.

日本語の概要は準備中です。原文の説明を表示しています。

Investigate an alert or incident in this channel: classify the symptom, load the matching triage reference, check lessons.md for known causes, and post a grounded first-pass diagnosis with evidence links and a proposed (never executed) fix. Use when an alert fires, a routine detects a new anomaly, or someone reports something broken ("tests aren't running", "deploys look stuck", "is CI down?").

日本語の概要は準備中です。原文の説明を表示しています。

Lint and typecheck debt paydown: clear pre-existing repo-wide lint/type violations and suppressions (@ts-ignore, # type: ignore) one rule per wave, fixing causes not symptoms. Runs on green main. For diff-scoped quality use absolute-simplify. Triggers on "absolute debt", "fix our lint warnings", "clear the type errors", "get to strict mode", "burn down suppressions".

日本語の概要は準備中です。原文の説明を表示しています。

Flaky test fixes: detect nondeterministic tests empirically (repeat/shuffle/parallel runs), diagnose the root cause, fix it — never retry/skip/sleep — and verify across many randomized runs. Triggers on "absolute deflake", "fix flaky tests", "CI is flaky", "this test fails randomly/intermittently".

日本語の概要は準備中です。原文の説明を表示しています。

Design and validate model-facing tool definitions with clear names, action-oriented descriptions, bounded JSON Schema parameters, explicit side effects, safe defaults, idempotency, errors, and realistic tests. Use when creating function-calling tools, MCP tools, agent actions, structured tool inputs, or when a model selects the wrong tool, invents arguments, or causes unsafe side effects.

日本語の概要は準備中です。原文の説明を表示しています。

Determine the TRUE cause of a failed retort run before attributing it. Ground-truth every failure (run its tests, read its agent logs, inspect its workspace) and classify it as an infrastructure false-fail, a genuine model miss, or an environment issue — never trust the gate verdict or a log signature alone. Use whenever a run is recorded failed (test_coverage=0 / gate fail), a pass rate looks low, or you're deciding whether a "failure" is real before reporting or proceeding.

日本語の概要は準備中です。原文の説明を表示しています。

Audit any Lattice SKILL.md for language compliance — removes rationale prose, converts soft language to imperatives, adds STOP: gates on hard rules, and cuts redundant repetition. Complements skill-review (which finds behavioral gaps) by fixing phrasing that causes agents to skip or underweight instructions at runtime. Use after writing or significantly changing any skill, or when the user says 'tighten this skill', 'clean up the language', 'make this more effective', 'reduce the bloat', 'tighten the language', or 'skill tighten'. Standalone — does not call other skills.

日本語の概要は準備中です。原文の説明を表示しています。

End-to-end post-GWAS causal inference pipeline orchestrating heritability partitioning, genetic correlation, Mendelian randomization with CHP-aware sensitivity (CAUSE / LHC-MR), colocalization, fine-mapping with SuSiE / FOCUS, mediation, TWAS triangulation, cis-pQTL drug-target MR, effector-gene prioritization (L2G / PoPS / cS2G), and GenomicSEM common-factor GWAS. Use when triangulating causal inference across multiple complementary methods, prioritizing tissues via stratified LDSC, nominating or de-risking drug targets, mapping a lead SNP to a candidate effector gene, modeling shared genetic architecture across correlated traits, or producing a STROBE-MR-compliant publication-grade evidence battery from GWAS summary statistics.

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Performs time-to-event analysis for clinical trials including Cox proportional hazards regression with PH diagnostics, restricted mean survival time (RMST) under non-PH, competing risks via Fine-Gray vs cause-specific Cox, weighted log-rank and MaxCombo for non-proportional hazards, recurrent events (Andersen-Gill, PWP, WLW), and interval-censored data. Use when analyzing time-to-event endpoints (OS, PFS, DOR, TTR, TTNT) in oncology or other clinical trials.

日本語の概要は準備中です。原文の説明を表示しています。

Internal — for Boundless team members only. Cross-reference Boundless indexer API data, broker telemetry, and service logs to investigate production and staging operational issues. Use when the user wants to understand why slashings happened on prod/staging, diagnose prover or service failures in deployed environments, correlate market events with broker behavior, investigate fulfillment rate drops, look at prover/service logs, or perform any analysis that requires combining on-chain indexer data with off-chain broker telemetry and CloudWatch logs. Also use when the user asks to "investigate", "diagnose", or "find root cause" for prover, service, or market issues on live networks. Do NOT use for debugging local code changes, reviewing PRs, or investigating issues in the codebase itself.

日本語の概要は準備中です。原文の説明を表示しています。

Analyze escaped defects and test suite health through blameless postmortems. Covers bug pattern analysis, test suite health reviews, 5 Whys root cause analysis, process improvement cycles, and postmortem/retro meeting templates with action item tracking. Use when: "QA retro," "escaped bugs," "postmortem," "quality incident," "defect analysis," "improvement cycle." Not for: live release go/no-go decisions — use release-readiness. Not for ongoing metric dashboards — use qa-metrics. Not for reviewing existing test code quality — use ai-qa-review. Related: qa-metrics, test-reliability, test-strategy.

日本語の概要は準備中です。原文の説明を表示しています。

Diagnose a failing CI workflow run (lint, markdown lint, build, or unit tests) — identify which job failed, the cause, and a concrete fix

日本語の概要は準備中です。原文の説明を表示しています。

Diagnose a failing TMDb integration-test run — summarise the failure, rank the likely cause, and suggest a concrete fix

日本語の概要は準備中です。原文の説明を表示しています。

Root cause based one-shot bug fix. debugger diagnosis → requirements.md generation → /execute. /bugfix "error description" Full investigation pipeline: debugger + gap-analyzer + standard verify. QA suggestion after successful fix.

日本語の概要は準備中です。原文の説明を表示しています。

Find what a change could break somewhere else before it ships, beyond the diff, and prove the one fact it's safe because of by running real code instead of writing it up. Use for 'blast radius of X', 'what could this break', or reviewing a small diff you don't trust.

日本語の概要は準備中です。原文の説明を表示しています。

Analyze indexed codebases via graph database (neug) and vector index (zvec). Covers call graphs, dependencies, dead code, hotspots, module coupling, architecture reports, semantic search, impact analysis, bug root cause from GitHub issues, class diagrams (UML), and PR review (risk scoring, conflict detection, auto-merge candidates, labeling). Also covers creating, inspecting, and repairing a CodeScope index. Use for: code structure, who calls what, why something changed, similar functions, module boundaries, bug tracing, class relationships, PR risk/conflicts, or any question benefiting from a code knowledge graph. Applies when a `.codegraph` index exists in the workspace, or when the user wants to create one.

日本語の概要は準備中です。原文の説明を表示しています。

Analyze application error logs and monitoring platform exports (Sentry, Datadog, Rollbar) to identify error patterns, duplicates, and severity. Use when someone asks to "audit errors", "analyze exceptions", "find noisy alerts", "deduplicate error groups", or "triage production errors". Parses JSON/CSV error exports and produces categorized reports with root cause analysis.

日本語の概要は準備中です。原文の説明を表示しています。

World-class motion design expertise combining Disney's 12 principles of animation, Material Design's motion system, and the performance-first philosophy of production interfaces. Motion design is the craft of bringing interfaces to life - not through decoration, but through clarity, continuity, and communication. Great motion design is invisible. Users don't notice the animations - they notice that the interface feels natural, responsive, and alive. Every transition tells a story: where did this come from? Where is it going? What just happened? Motion is the language of cause and effect in digital interfaces. Use when "animation, motion, transition, microinteraction, easing, timing, choreography, loading animation, skeleton loader, page transition, hover effect, scroll animation, spring animation, keyframes, framer motion, gsap, lottie, animation, motion, transitions, microinteractions, easing, timing, accessibility, performance, frontend, design" mentioned.

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Core marketing principles for startups. Covers positioning, messaging, channels, and growth. Focus on what works for resource-constrained teams. Battle scars included: I've watched startups burn $1M on Instagram ads because "that's where competitors are", seen "growth hacks" destroy brand trust overnight, and advised founders who scaled marketing before finding product-market fit. Contrarian take: Most startup marketing advice is backwards. The best marketing doesn't feel like marketing. Distribution comes before product features. And your first 100 customers should never come from paid ads. Use when "marketing strategy, positioning, messaging, customer acquisition, growth channels, brand, go-to-market, " mentioned.

日本語の概要は準備中です。原文の説明を表示しています。